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Seeking an optimal operational regime under different management environments has been one of the main concerns of forest managers. Traditionally, the main operational regime includes planting density or regeneration scheme, thinning time/intensity, and optimal time to harvest over the given time horizon. Deterministic approaches to tackle this type of optimization problem with different controls have dominated the solution techniques in forestry literature. We present in this paper an overview of the methodologies used in stand-level optimization, in which we show the strengths and weaknesses of these methodologies as well as provide comments on the effectiveness of the methodology. We then propose a new dynamic programing approach for generalizing solution specification and techniques.

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The choice of a baseline against which to evaluate changes in carbon stocks is a critical component of any forest carbon offset market. In this paper, we use a discrete dynamic programming model and data from a lodgepole pine (Pinus contorta Douglas ex Loudon) stand in northeastern British Columbia, Canada, to demonstrate that different baselines have little or no effect on optimal harvest decision but can have a large impact on economic returns to a landowner. The results reveal that the magnitude of the financial return to the landowner is dependent on the starting conditions of both the predetermined baseline and the proposed carbon offset project. The study also shows that when given the choice between alternative baselines, a landowner will always choose a fixed baseline over a business-as-usual baseline.